AutoDetect:

Towards a Unified Framework for Automated Weakness Detection in Large Language Models

🤗 Data • 📃 Paper

Effective weakness discovery can well guide model enhancement. AutoDetect can achieve high identification success rates in the instruction-following, mathematics and coding tasks (A). Moreover, leveraging this data, we can further improve LLMs (B).

Table of Contents

• Data
• Quick Start
  • Setup
  • Weakness Detection
• Citation

Data

Our dataset can be found on Hugging Face.

We show data from the Autodetect process for the models listed in the paper, each in the following format:

{
    "key_point": {key_point},
    "prompt": {prompt},
    "answer": {answer},
    "ref_ans": {ref_ans},
    "comparison": {comparison},
    "score": {score}
}

• {prompt}: The question used to evaluate the target LLM.
• {key_point}: The summary of the key knowledge encapsulated in the {prompt}.
• {answer}: The response generated by the target LLM.
• {ref_ans}: The reference response generated by GPT-4 Turbo.
• {comparison}: The comparison output from the LLM-judge (GPT-4 Turbo).
• {score}: The final evaluation score, ranging from 1 to 10.

By leveraging the prompt alongside the ref_ans, we can effectively enhance the performance of the target LLM.

Quick Start

For all codes, we have added #TODO comments to indicate places in the code that need modification before running. Please update the relevant parts as noted before executing each file.

Setup

pip install -r requirements.txt

Weakness Detection

We provide the code for AutoDetect on Instruction-following, Mathematics and Coding tasks, the usage of which are exactly the same.

Here we take Instruction-following task for example. To run AutoDetect, please run the following command.

cd autodetect_if/scripts
bash autodetect_if

The result will be stored under the path autodetect_if/result.

To calculate the weakness identification success rate, run the following command:

python count_res.py

Acknowledgement

• Fine-tuning code: llm_finetuning
• Evaluation scripts: IFEval, MetaMath, and HumanEval

Citation

@article{cheng2024autodetect,
  title={AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models},
  author={Cheng, Jiale and Lu, Yida and Gu, Xiaotao and Ke, Pei and Liu, Xiao and Dong, Yuxiao and Wang, Hongning and Tang, Jie and Huang, Minlie},
  journal={arXiv preprint arXiv:2406.16714},
  year={2024}
}